and Engineering) is investigated using machine learning and biometrics to measure the emotional and behavioral states of students in the classroom. The approach collects multi-dimensional biometrics via camera and wristband monitors of facial expressions, eye gaze, hand/head/body movement, and heart rate. From these data, a software model is trained to classify student engagement.
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